The nature of science is changing dramatically, from single researcher at a lab or university laboratory working with graduate students to a distributed multi- researcher consortiums, across universities and research labs, tackling large scientific problems. In addition, experimentalists and theorists are collaborating with each other by designing experiments to prove the proposed theories. ‘Big Data’ being produced by these large experiments have to verified against simulations run on High Performance Computing (HPC) resources.

The trends above are pointing towards

Geographically dispersed experiments (and associated communities) that require data being moved across multiple sites. Appropriate mechanisms and tools need to be employed to move, store and archive datasets from such experiments.

Convergence of simulation (requiring High Performance Computing) and Big Data Analytics (requiring advanced on-site data management techniques) into a small number of High Performance Computing centers. Such centers are key for consolidating software and hardware infrastructure efforts, and achieving broad impact across numerous scientific domains.

The trends indicate that for modern science and scientific discovery, infrastructure support for handling both large scientific data as well as high-performance computing is extremely important. In addition, given the distributed nature of research and big-team science, it is important to build infrastructure, both hardware and software, that enables sharing across

institutions, researchers, students, industry and academia. This is the only way that a nation can maximize the research capabilities of its citizens while maximizing the use of its investments in computer, storage, network and experimental infrastructure.

This chapter introduces infrastructure requirements of High-Performance Computing and Networking with examples drawn from NERSC and ESnet, two large Department of Energy facilities at Lawrence Berkeley National Laboratory, CA, USA, that exemplify some of the qualities needed for future Research & Education infrastructure.

Shared research infrastructure that is globally distributed and widely accessible has been a hallmark of the networking community. We present a vision for a future mid-scale distributed research infrastructure aimed at enabling new types of discoveries. The “lessons learned” from constructing and operating the Global Environment for Network Innovations (GENI) infrastructure are the basis for our attempt to project future concepts and solutions. Our aim is to engage the community to contribute new ideas and to inform funding agencies about future research directions.

Attacks against network infrastructures can be detected by Intrusion Detection Systems (IDS). Still reaction to these events are often limited by the lack of larger contextual information in which they occurred. In this paper we present CoreFlow, a framework for the correlation and enrichment of IDS data with network flow information. CoreFlow ingests data from the Bro IDS and augments this with flow data from the devices in the network. By doing this the network providers are able to reconstruct more precisely the route followed by the malicious flows. This enables them to devise tailored countermeasures, e.g. blocking close to the source of the attack. We tested the initial CoreFlow prototype in the ESnet network, using inputs from 3 Bro systems and more than 50 routers.

In this paper, we discuss building blocks that enable the exploitation of optical capacities beyond 100 Gb∕s. Optical networks will benefit from more flexibility and agility in their network elements, especially from co- herent transceivers. To achieve capacities of 400 Gb∕s and more, coherent transceivers will operate at higher symbol rates. This will be made possible with higher bandwidth components using new electro-optic technologies imple- mented with indium phosphide and silicon photonics. Digital signal processing will benefit from new algorithms. Multi-dimensional modulation, of which some formats are already in existence in current flexible coherent transceiv- ers, will provide improved tolerance to noise and fiber non- linearities. Constellation shaping will further improve these tolerances while allowing a finer granularity in the selection of capacity. Frequency-division multiplexing will also provide improved tolerance to the nonlinear charac- teristics of fibers. Algorithms with reduced computation complexity will allow the implementation, at speeds, of direct pre-compensation of nonlinear propagation effects. Advancement in forward error correction will shrink the performance gap with Shannon’s limit. At the network con- trol and management level, new tools are being developed to achieve a more efficient utilization of networks. This will also allow for network virtualization, orchestration, and management. Finally, FlexEthernet and FlexOTN will be put in place to allow network operators to optimize capac- ity in their optical transport networks without manual changes to the client hardware.

Service providers and vendors are moving toward a network virtualized core, whereby multiple applications would be treated on their own merit in programmable hardware. Such a network would have the advantage of being customized for user requirements and allow provisioning of next generation services that are built speci cally to meet user needs. In this article, we articulate the impact of network virtualization on networks that provide customized services and how a pro- vider’s business can grow with network virtualization. We outline a decision map that allows mapping of applications with technology that is supported in network-virtualization--oriented equipment. Analogies to the world of virtual machines and generic virtualization show that hardware supporting network virtualization will facilitate new customer needs while optimizing the provider network from the cost and performance perspectives. A key conclusion of the article is that growth would yield sizable revenue when providers plan ahead in terms of supporting network-virtualization-oriented technology in their networks. To be precise, providers have to incorporate into their growth plans network elements capable of new service deployments while protecting network neutrality. A simulation study validates our NV-induced model.

The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of ‘big data’ driven by Exascale, cloud computing, machine learning and AI. The project’s architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive ‘intent’ based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a firstclass schedulable resource akin to instruments, compute, and storage, to enable well defined and highly tuned complex workflows that require close coupling of resources spread across a vast geographic footprint such as those used in science domains like high-energy physics and basic energy sciences.

This paper introduces ExoPlex, a framework to improve the QoS of live (real) experiments on the ExoGENI federated testbed. The authors make the case for implementing the abstraction of network service providers (NSPs) as a way of having experimenters specify the performance characteristics they expect from the platform (at the testbed level). An example tenant using this version of ExoGENI enhanced with NSP capabilities is presented, and experimental results show the effectiveness of the approach.

This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data

We evaluate a WAN-virtualization framework in terms of delay and scalability and demonstrate that adding a virtual layer between the physical topology and the end-user brings significant advantages and tolerable delays

National Research and Education Networks (NRENs) are becoming keener in providing information on the energy consumption of their equipment. However there are only few NRENs trying to use the available information to reduce power consumption and/or carbon footprint. We set out to study the impact that deploying energy-aware networking devices may have in terms of CO2 emissions, taking the ESnet network as use case. We defined a model that can be used to select paths that lead to a lower impact on the CO2 footprint of the network. We implemented a simulation of the ESnet network using our model to investigate the CO2 footprint under different traffic conditions. Our results suggest that NRENs such as ESnet could reduce their network’s environmental impact if they would deploy energy- aware hardware combined with paths setup tailored to reduction of carbon footprint. This could be achieved by modification of the current path provisioning systems used in the NREN community.

Wide area networks (WAN) forward traffic through a mix of packet and optical data planes, composed by a variety of devices from different vendors. Multiple forwarding technologies and encapsulation methods are used for each data plane (e.g. IP, MPLS, ATM, SONET, Wavelength Switching). Despite standards defined, the control planes of these devices are usually not interoperable, and different technologies are used to manage each forwarding segment independently (e.g. OpenFlow, TL-1, GMPLS). The result is lack of coordination between layers and inefficient resource usage. In this paper we discuss the design and implementation of a system that uses unmodified OpenFlow to optimize network utilization across layers, enabling practical bandwidth virtualization. We discuss strategies for scalable traffic monitoring and to minimize losses on route updates across layers. We explore two use cases that benefit from multi-layer bandwidth on demand provisioning. A prototype of the system was built open using a traditional circuit reservation application and an unmodified SDN controller, and its evaluation was per-formed on a multi-vendor testbed.

Advances in optical communications and switching technologies are enabling energy-efficient, flexible, higher- utilization network operations. To take full advantage of these capabilities, the scope of optical circuit networks can be increased in both the vertical and horizontal directions. In the vertical direction, some of the existing Internet applications, transport-layer protocols, and application-programming interfaces need to be redesigned and new ones invented to leverage the high-bandwidth, low-latency capabilities of optical circuit networks. In the horizontal direction, inter-domain control and management-protocols are required to create a global-scale interconnection of optical circuit-switched networks.

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that create an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

There have been a lot of proposals to unify the control and management of packet and circuit networks but none have been deployed widely. In this paper, we propose a sim- ple programmable architecture that abstracts a core transport node into a programmable virtual switch, that meshes well with the software-defined network paradigm while leverag- ing the OpenFlow protocol for control. A demonstration use-case of an OpenFlow-enabled optical virtual switch im- plementation managing a small optical transport network for big-data applications is described. With appropriate exten- sions to OpenFlow, we discuss how the programmability and flexibility SDN brings to packet-optical backbone networks will be substantial in solving some of the complex multi- vendor, multi-layer, multi-domain issues service providers face today.

Several studies have proposed job migration over the wide area network (WAN) to reduce the energy of networks of datacenters by taking advantage of different electricity prices and load demands. Each study focuses on only a small subset of network parameters and thus their results may have large errors. For example, datacenters usually have long-term power contracts instead of paying market prices. However, previous work neglects these contracts, thus overestimating the energy savings by 2.3x. We present a comprehensive approach to minimize the energy cost of networks of datacenters by modeling performance of the workloads, power contracts, local renewable energy sources, different routing options for WAN and future router technologies. Our method can reduce the energy cost of datacenters by up to 28%, while reducing the error in the energy cost estimation by 2.6x.

Providing high-speed data transfer is vital to various data-intensive applications. While there have been remarkable technology advances to provide ultra-high-speed network band- width, existing protocols and applications may not be able to fully utilize the bare-metal bandwidth due to their inefficient design. We identify the same problem remains in the field of Remote Direct Memory Access (RDMA) networks. RDMA offloads TCP/IP protocols to hardware devices. However, its benefits have not been fully exploited due to the lack of efficient software and application protocols, in particular in wide-area networks. In this paper, we address the design choices to develop such protocols. We describe a protocol implemented as part of a communication middleware. The protocol has its flow control, connection management, and task synchronization. It maximizes the parallelism of RDMA operations. We demonstrate its performance benefit on various local and wide-area testbeds, including the DOE ANI testbed with RoCE links and InfiniBand links.

University campuses, Supercomputer centers and R&E networks are challenged to architect, build and support IT infrastructure to deal effectively with the data deluge facing most science disciplines. Hybrid network architecture, multi-domain bandwidth reservations, performance monitoring and GLIF Open Lightpath Exchanges (GOLE) are examples of network architectures that have been proposed, championed and implemented successfully to meet the needs of science. Most recently, Science DMZ, a campus design pattern that bypasses traditional performance hotspots in typical campus network implementation, has been gaining momentum. In this paper and corresponding demonstration, we build upon the SC11 SCinet Research Sandbox demonstrator with Software-Defined networking to explore new architectural approaches. A virtual switch network abstraction is explored, that when combined with software-defined networking concepts provides the science users a simple, adaptable network framework to meet their upcoming application requirements.

Abstract—Data set sizes are growing exponentially, so it is important to use data movement protocols that are the most efficient available. Most data movement tools today rely on TCP over sockets, which limits flows to around 20Gbps on today’s hardware. RDMA over Converged Ethernet (RoCE) is a promising new technology for high-performance network data movement with minimal CPU impact over circuit-based infrastructures. We compare the performance of TCP, UDP, UDT, and RoCE over high latency 10Gbps and 40Gbps network paths, and show that RoCE-based data transfers can fill a 40Gbps path using much less CPU than other protocols. We also show that the Linux zero-copy system calls can improve TCP performance considerably, especially on current Intel “Sandy Bridge”-based PCI Express 3.0 (Gen3) hosts.

100Gbps networking has finally arrived, and many research and educational institutions have begun to deploy 100Gbps routers and services. ESnet and Internet2 worked together to make 100Gbps networks available to researchers at the Supercomputing 2011 conference in Seattle Washington. In this paper, we describe two of the first applications to take advantage of this network. We demonstrate a visualization application that enables remotely located scientists to gain insights from large datasets. We also demonstrate climate data movement and analysis over the 100Gbps network. We describe a number of application design issues and host tuning strategies necessary for enabling applications to scale to 100Gbps rates.

ESnet provides a platform for moving large data sets and accelerating worldwide scientific collaboration. It provides high-bandwidth, reliable connections that link scientists at national laboratories, universities and other research institutions, enabling them to collaborate on some of the world's most important scientific challenges including renewable energy sources, climate science, and the origins of the universe.

ESnet has embarked on a major project to provide substantial visibility into the inner-workings of the network by aggregating diverse data sources, exposing them via web services, and visualizing them with user-centered interfaces. The portal’s strategy is driven by understanding the needs and requirements of ESnet’s user community and carefully providing interfaces to the data to meet those needs. The 'MyESnet Portal' allows users to monitor, troubleshoot, and understand the real time operations of the network and its associated services.

This paper will describe the MyESnet portal and the process of developing it. The data for the portal comes from a wide variety of sources: homegrown systems, commercial products, and even peer networks. Some visualizations from the portal are presented highlighting some interesting and unusual cases such as power consumption and flow data. Developing effective user interfaces is an iterative process. When a new feature is released, users are both interviewed and observed using the site. From this process valuable insights were found concerning what is important to the users and other features and services they may also want. Open source tools were used to build the portal and the pros and cons of these tools are discussed.

Abstract: Many companies deploy multiple data centers across the globe to satisfy the dramatically increased computational demand. Wide area connectivity between such geographically distributed data centers has an important role to ensure both the quality of service, and, as bandwidths increase to 100Gbps and beyond, as an efficient way to dynamically distribute the computation. The energy cost of data transmission is dominated by the router power consumption, which is unfortunately not energy proportional. In this paper we not only quantify the performance benefits of leveraging the network to run more jobs, but also analyze its energy impact. We compare the benefits of redesigning routers to be more energy efficient to those obtained by leveraging locally available green energy as a complement to the brown energy supply. Furthermore, we design novel green energy aware routing policies for wide area traffic and compare to state-of-the-art shortest path routing algorithm. Our results indicate that using energy proportional routers powered in part by green energy along with our new routing algorithm results in 10x improvement in per router energy efficiency with 36% average increase in the number of jobs completed.

Zurawski, J., Swany M., and Gunter D.,“A Scalable Framework for Representation and Exchange of Network Measurements”,2nd International IEEE/Create-Net Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TridentCom 2006),Barcelona, Spain,2006,

The book at hand gives an overview of the state of the art research in Computational Sustainability as well as case studies of different application scenarios. This covers topics such as renewable energy supply, energy storage and e-mobility, efficiency in data centers and networks, sustainable food and water supply, sustainable health, industrial production and quality, etc. The book describes computational methods and possible application scenarios.

2015

Mariam Kiran,“What is Modelling and Simulation: An introduction”,Encyclopedia of Computer Science and Technology,
(
December 24, 2015)

2008

William Johnston, Evangelos Chaniotakis, Eli Dart, Chin Guok, Joe Metzger, Brian Tierney,“The Evolution of Research and Education Networks and their Essential Role in Modern Science”,Trends in High Performance & Large Scale Computing,
(
November 1, 2008)

ABSTRACT In widely distributed systems generally, and in science-oriented Grids in particular, software, CPU time, storage, etc., are treated as “services” – they can be allocated and used with service guarantees that allows them to be integrated into systems that perform complex tasks. Network communication is currently not a service – it is provided, in general, as a “best effort” capability with no guarantees and only statistical predictability.

In order for Grids (and most types of systems with widely distributed components) to be successful in performing the sustained, complex tasks of large-scale science – e.g., the multi-disciplinary simulation of next generation climate modeling and management and analysis of the petabytes of data that will come from the next generation of scientific instrument (which is very soon for the LHC at CERN) – networks must provide communication capability that is service-oriented: That is it must be configurable, schedulable, predictable, and reliable. In order to accomplish this, the research and education network community is undertaking a strategy that involves changes in network architecture to support multiple classes of service; development and deployment of service-oriented communication services, and; monitoring and reporting in a form that is directly useful to the application-oriented system so that it may adapt to communications failures.

In this paper we describe ESnet's approach to each of these – an approach that is part of an international community effort to have intra-distributed system communication be based on a service-oriented capability.

This report summarizes an effort called "The ESnet Amazon Web Services (AWS) pilot" which was implemented to determine AWS “Direct Connect” or “DX” service provides advantages to ESnet customers above and beyond that of ESnet's standard Amazon connections at public Internet exchange points.

Traditionally, WAN and campus networks and services have evolved independently from each other. For example, MPLS traffic engineered and VPN technologies have been targeted towards the WAN, while the LAN (or last mile) implementations have not incorporated that functionality. These restrictions have resulted in dissonance in services offered in the WAN vs. the LAN. While OSCARS/NSI virtual circuits are widely deployed in the WAN, they typically only run from site boundary to site boundary, and require painful phone calls, manual configuration, and resource allocation decisions for last mile extension. Such inconsistencies in campus infrastructures, all the way from the campus edge to the data-transfer hosts, often lead to unpredictable application performance. New architectures such as the Science DMZ have been successful in simplifying the variance, but the Science DMZ is not designed or able to solve the end-to-end orchestration problem. With the advent of SDN, the R&E community has an opportunity to genuinely orchestrate end-to-end services - and not just from a network perspective, but also from an end-host perspective. In addition, with SDN, the opportunity exists to create a broader set of custom intelligent services that are targeted towards specific science application use-cases. This proposal describes an advanced deployment of SDN equipment and creation of a comprehensive SDN software platform that will help with bring together the missing end-to-end story.

SDN has been successfully implemented by large companies and ISPs within their own data centers. However, the focus has remained on intradomain use cases with controllers under the purview of the same authority. Interdomain SDN promises more fine grained control of data flows between SDN networks but also presents the greater challenges of trust, authentication and policy control between them. We propose a secure method to peer SDN networks and a test implementation

Michael Smitasin, Brian Tierney,Switch Buffers Experiments: How much buffer do you need to support 10G flows?,2014 Technology Exchange,October 29, 2014,

The Science DMZ model is a widely deployed and accepted architecture allowing for movement and sharing of large-scale data sets between facilities, resources, or institutions. In order to help assure integrity of the resources served by the science DMZ, a different approach should be taken regarding necessary resources, visibility as well as perimeter and host security. Experienced panelists discuss common techniques, best practices, typical caveats as well as what to expect (and not expect) from a network perimeter that is purpose built to move science data.

The Science DMZ model is a widely deployed and accepted architecture allowing for movement and sharing of large-scale data sets between facilities, resources, or institutions.In order to help assure integrity of the resources served by the science DMZ, a different approach should be taking regardingnecessary resources, visibility as well as perimeter and host security. Based on proven and existing production techniquesand deployment strategies, we provide an operational map and high level functional framework for securing a science DMZ utilizing a “defense in depth” strategy including log aggregation, effective IDS filtering and management techniques, black hole routing,flow data and traffic baselining.

University campuses, Supercomputer centers and R&E networks are challenged to architect, build and support IT infrastructure to deal effectively with the data deluge facing most science disciplines. Hybrid network architecture, multi-domain bandwidth reservations, performance monitoring and GLIF Open Lightpath Exchanges (GOLE) are examples of network architectures that have been proposed, championed and implemented successfully to meet the needs of science. This talk explores a new "one virtual switch" abstraction leveraging software-defined networking and OpenFlow concepts, that provides the science users a simple, adaptable network framework to meet their future application requirements. The talk will include the high-level design that includes use of OpenFlow and OSCARS as well as implementation details from demonstration planned for super-computing.

The emerging era of “Big Science” demands the highest possible network performance. End-to-end circuit automation and workflow-driven customization are two essential capabilities needed for networks to scale to meet this challenge. This demonstration showcases how combining software-defined networking techniques with virtual circuits capabilities can transform the network into a dynamic, customer-configurable virtual switch. In doing so, users are able to rapidly customize network capabilities to meet their unique workflows with little to no configuration effort. The demo also highlights how the network can be automated to support multiple collaborations in parallel.

- What do you envision will have dramatic impact in the future networking and data management? What research challenges do you expect in achieving your vision?

- Do we need to re-engineer existing tools and middleware software? Elaborate on network management middleware in terms of virtual circuits, performance monitoring, and diagnosis tools.

- How do current applications match increasing data sizes and enhancements in network infrastructure? Please list a few network-aware application. What is the scope of networking in the application domain?

- Resource management and scheduling problems are gaining importance due to current developments in utility computing and high interest in Cloud infrastructure. Explain your vision. What sort of algorithms/mechanisms will practically be used in the future?

- What are the main issues in designing/modelling cutting edge dynamic networks for large-scale data processing? What sort of performance problems do you expect?- What necessary step do we need to implement to benefit from next generation high bandwidth networks? Do you think there will be radical changes such as novel APIs or new network stacks?

Mike Bennett,Energy Efficiency in IEEE Ethernet Networks – Current Status and Prospects for the Future,Joint ITU/IEEE Workshop on Ethernet--Emerging Applications and Technologies,September 2012,

Greg Bell,Network as Instrument,NORDUnet 2012,September 2012,

Bill Johnston, Eli Dart, and Brian Tierney,Addressing the Problem of Data Mobility for Data-Intensive Science: Lessons Learned from the data analysis and data management systems of the LHC,ECT2012: The Eighth International Conference on Engineering Computational Technology,September 2012,

Bill Johnston, Eli Dart, and Brian Tierney,Addressing the Problem of Data Mobility for Data-Intensive Science: Lessons Learned from the data analysis and data management systems of the LHC,ARNES: The Academic and Research Network of Slovenia,August 2012,

In this talk, Acting Director Greg Bell will provide an update on ESnet's recent activities through the lens of its mission to accelerate discovery for researchers in the DOE Office of Science. Topics covered: what makes ESnet distinct? Why does its ScienceDMZ strategy matter? What are potential 'design patterns' for data-intensive science? Does 100G matter?

On-demand Secure Circuits and Advance Reservation System (OSCARS) has evolved tremendously since its conception as a DOE funded project to ESnet back in 2004. Since then, it has grown from a research project to a collaborative open-source software project with production deployments in several R&E networks including ESnet and Internet2. In the latest release of OSCARS as version 0.6, the software was redesigned to flexibly accommodate both research and production needs. It is being used currently by several research projects to study path computation algorithms, and demonstrate multi-layer circuit management. Just recently, OSCARS 0.6 was leveraged to support production level bandwidth management in the ESnet ANI 100G prototype network, SCinet at SC11 in Seattle, and the Internet2 DYNES project. This presentation will highlight the evolution of OSCARS, activities surrounding OSCARS v0.6 and lessons learned, and share with the community the roadmap for future development that will be discussed within the open-source collaboration.

This presentation will discuss the challenges and lessons learned in the deployment of the 100GigE ANI Prototype network and support of 100G circuit services during SC11 in Seattle. Interoperability, testing, measurement, debugging, and operational issues at both the optical and layer-2/3 will be addressed.
Specific topics will include:
(1) 100G pluggable optics – options, support, and standardization issues,
(2) Factors negatively affecting 100G line-side transmission,
(3) Saturation testing and measurement with hosts connected at 10G,
(4) Debugging and fault isolation with creative use of loops/circuit services,
(5) Examples of interoperability problems in a multi-vendor environment, and
(6) Case study: Transport of 2x100G waves to SC11.

There are several aspects to building successful infrastructure to support data intensive science. The Science DMZ Model incorporates three key components into a cohesive whole: a high-performance network architecture designed for ease of use; well-configured systems for data transfer; and measurement hosts to provide visibility and rapid fault isolation. This tutorial will cover aspects of network architecture and network device configuration, the design and configuration of a Data Transfer Node, and the deployment of perfSONAR in the Science DMZ. Aspects of current deployments will also be discussed.

Todays science collaborations depend on reliable, high performance networks, but monitoring the end-to-end performance of a network can be costly and difficult. The most accurate approaches involve using measurement equipment in many locations, which can be both expensive and difficult to manage due to immobile or complicated assets.

The perfSONAR framework facilitates network measurement making management of the tests more reasonable. Traditional deployments have used over-provisioned servers, which can be expensive to deploy and maintain. As scientific network uses proliferate, there is a desire to instrument more facets of a network to better understand trends.

This work explores low cost alternatives to assist with network measurement. Benefits include the ability to deploy more resources quickly, and reduced capital and operating expenditures. We present candidate platforms and a testing scenario that evaluated the relative merits of four types of small form factor equipment to deliver accurate performance measurements.

The Belle experiment, part of a broad-based search for new physics, is a collaboration of ~400 physicists from 55 institutions across four continents. The Belle detector is located at the KEKB accelerator in Tsukuba, Japan. The Belle detector was operated at the asymmetric electron-positron collider KEKB from 1999-2010. The detector accumulated more than 1 ab-1 of integrated luminosity, corresponding to more than 2 PB of data near 10 GeV center-of-mass energy. Recently, KEK has initiated a $400 million accelerator upgrade to be called SuperKEKB, designed to produce instantaneous and integrated luminosity two orders of magnitude greater than KEKB. The new international collaboration at SuperKEKB is called Belle II. The first data from Belle II/SuperKEKB isexpected in 2015.

In October 2012, senior members of the Belle-II collaboration gathered at PNNL to discuss the computing and neworking requirements of the Belle-II experiment with ESnet staff and other computing and networking experts. The day-and-a-half-long workshop characterized the instruments and facilities used in the experiment, the process of science for Belle-II, and the computing and networking equipment and configuration requirements to realize the full scientific potential of the collaboration’s work.

The requirements identified at the Belle II Experiment Requirements workshop are summarized in the Findings section, and are described in more detail in this report. KEK invited Belle II organizations to attend a follow-up meeting hosted by PNNL during SC12 in Salt Lake City on November 13, 2012. The notes from this meeting are in Appendix C.

2003

Report of the June 3-5, 2003, DOE Science Networking Workshop Conducted by the Energy Sciences Network Steering Committee at the request of the Office of Advanced Scientific Computing Research of the U.S. Department of Energy Office of Science.